Reconstruction based face occlusion elimination for recognition

SRC is a recent breakthrough on occluded face recognition, but raw SRC is slow and inaccurate because both occlusion elimination and face recognition are confused and finished once in ℓ1-optimizationℓ1-optimization. In this paper, a reconstruction based occlusion elimination and then recognition framework is put forward. The occlusion elimination procedure is consisted of two consecutive parts, occlusion detection and face reconstruction, where SRC is only used during occlusion detection. Specifically, downsampled SRC is utilized first of all to locate possible face occlusion at low computing complexity, and then the discovered unoccluded face pixels are imported into an overdetermined equation system to reconstruct an intact face. In this approach, since occlusion detection is independent with recognition, it could be carried out on downsampled images to effectively reduce computing complexity at ignorable accuracy loss. After occlusion elimination, all state of the art general recognition approaches, such as CRC_RLS, LDA, and LPP, could be directly utilized to improve classification accuracy. The verification experiments are conducted on both simulated and genuine occlusion.